检索结果-内蒙古大学图书馆

An Intra-Server Interconnect Fabric for Heterogeneous computing

Journal of computer Science & technology 2014年第6期29卷 976-988页

作者：曹政Zheng Cao 刘小丽李强刘小兵王展安学军 CCF ACM State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences

With the increasing diversity of application needs and computing units, the server with heterogeneous pro- cessors is more and more widespread. However, conventional SMP/ccNUMA server architecture introduces communication bottleneck between heterogeneous processors and only uses heterogeneous processors as coprocessors, which limits the efficiency and flexibility of using heterogeneous processors. To solve this problem, this paper proposes an intra-server inter- connect fabric that supports both intra-server peer-to-peer interconnection and I/O resource sharing among heterogeneous processors. By connecting processors and I/O devices with the proposed fabric, heterogeneous processors can perform direct communication with each other and run in stand-alone mode with shared intra-server resources. We design the proposed fabric by extending the de-facto system I/O bus protocol PCIe （Peripheral computer Interconnect Express） and implement it with a single chip cZodiac. By making full use of PCIe＇s original advantages, the interconnection and the I/O sharing mechanism are light weight and efficient. Evaluations that have been carried out on both the FPGA （Field Programmable Gate Array） prototype and the cycle-accurate simulator demonstrate that our design is feasible and scalable. In addition, our design is suitable for not only the heterogeneous server but also the high density server.

关键词： heterogeneous system interconnection I/O virtualization PCI-express

来源：评论

学校读者我要写书评

暂无评论

Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zhigang Fan, Shaojing Liu, Zhenguang Wu, Zheqi Wu, Sifan Jiao, Yingying College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatio-temporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset. © 2025 IEEE.

关键词： Large datasets

来源：评论

学校读者我要写书评

暂无评论

Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

arXiv

引用

arXiv 2025年

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

引用

IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

来源：评论

学校读者我要写书评

暂无评论

Quantum memory assisted entangled state verification with local measurements

引用

Physical Review Research 2025年第1期7卷 013003-013003页

作者： Siyuan Chen Wei Xie Ping Xu Kun Wang Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei 230026 China College of Physics Jilin University Changchun 130012 China School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China

We consider the quantum memory assisted quantum state verification task, where an adversary prepares independent multipartite entangled states and sends them to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make a decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multiqubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying Greenberger-Horne-Zeilinger (GHZ)-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, shedding light on error-resistant strategies and practical applications of large-scale quantum memory assisted verification.

关键词： Quantum entanglement

来源：评论

学校读者我要写书评

暂无评论

Revisiting Multiple Pattern Matching Algorithms for Multi-Core architecture

引用

Journal of computer Science & technology 2011年第5期26卷 866-874页

作者：谭光明刘萍卜东波刘燕兵 Key Laboratory of Computer System and Architecture Institute of Computing TechnologyChinese Academy of Sciences Key Laboratory of Network Technology Institute of Computing TechnologyChinese Academy of Sciences

Due to the huge size of patterns to be searched,multiple pattern searching remains a challenge to several newly-arising applications like network intrusion *** this paper,we present an attempt to design efficient multiple pattern searching algorithms on multi-core *** observe an important feature which indicates that the multiple pattern matching time mainly depends on the number and minimal length of *** multi-core algorithm proposed in this paper leverages this feature to decompose pattern set so that the parallel execution time is *** formulate the problem as an optimal decomposition and scheduling of a pattern set,then propose a heuristic algorithm,which takes advantage of dynamic programming and greedy algorithmic techniques,to solve the optimization *** results suggest that our decomposition approach can increase the searching speed by more than 200% on a 4-core AMD Barcelona system.

关键词： parallel algorithm multi-core multiple pattern matching

来源：评论

学校读者我要写书评

暂无评论

Godson-T:An Efficient Many-Core architecture for Parallel Program Executions

引用

Journal of computer Science & technology 2009年第6期24卷 1061-1073页

作者：范东睿袁楠张军超周永彬林伟宋风龙叶笑春黄河余磊龙国平张浩刘磊 Key Laboratory of Computer Systems and Architecture Institute of Computing Technology Chinese Academy of Sciences

Moore＇s law will grant computer architects ever more transistors for the foreseeable future, and the challenge is how to use them to deliver efficient performance and flexible programmability. We propose a many-core architecture, Godson- T, to attack this challenge. On the one hand, Godson-T features a region-based cache coherence protocol, asynchronous data transfer agents and hardware-supported synchronization mechanisms, to provide full potential for the high efficiency of the on-chip resource utilization. On the other hand, Godson-T features a highly efficient runtime system, a Pthreadslike programming model, and versatile parallel libraries, which make this many-core design flexibly programmable. This hardware/software cooperating design methodology bridges the high-end computing with mass programmers. Experimental evaluations are conducted on a cycle-accurate simulator of Godson-T. The results show that the proposed architecture has good scalability, fast synchronization, high computational efficiency, and flexible programmability.

关键词： many-core parallel computing multithread data communication thread synchronization runtime system

来源：评论

学校读者我要写书评

暂无评论

Continual Reinforcement Learning for Digital Twin Synchronization Optimization

引用

IEEE Transactions on Mobile computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

DFMC:Feature-Driven Data-Free Knowledge Distillation

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Wenhao Chen, Shunpeng Xu, Shibiao Xu, Guangyuan Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from teacher networks without access to the real dataset. However, generator-based DFKD methods often suffer from insufficient diversity or low-confidence in synthetic images, negatively impacting student network performance. This paper introduces DFMC, a generative feature-driven framework to mitigate the inherent limitations of DFKD. We propose exploiting semantic description between generative feature domains to guide augmentation strategies, avoiding random abstract inputs caused by inconsistent semantic quality. Then, by applying noise to the generative features, we produce contrastive learning pairs indirectly, limiting the sampling range of the feature domain to encourage the student network to learn domain-invariant features. Finally, we guide the student network to deeply mimic the teacher’s layer-wise implicit classification behavior for the augmented synthetic images. Extensive experiments across various datasets and downstream tasks demonstrate the effectiveness of DFMC, achieving significant improvements while preventing student networks from overfitting to semantic ambiguous images. © 1991-2012 IEEE.

关键词： Knowledge transfer

来源：评论

学校读者我要写书评

暂无评论

A General-Purpose Many-Accelerator architecture Based on Dataflow Graph Clustering of Applications

引用

Journal of computer Science & technology 2014年第2期29卷 239-246页

作者：陈鹏张磊韩银和陈云霁 State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences University of Chinese Academy of Sciences

The combination of growing transistor counts and limited power budget within a silicon die leads to the utilization wall problem （a.k.a. ＂Dark Silicon＂）, that is only a small fraction of chip can run at full speed during a period of time. Designing accelerators for specific applications or algorithms is considered to be one of the most promising approaches to improving energy-efficiency. However, most current design methods for accelerators are dedicated for certain applications or algorithms, which greatly constrains their applicability. In this paper, we propose a novel general-purpose many-accelerator architecture. Our contributions are two-fold. Firstly, we propose to cluster dataflow graphs （DFGs） of hotspot basic blocks （BBs） in applications. The DFG clusters are then used for accelerators design. This is because a DFC is the largest program unit which is not specific to a certain application. We analyze 17 benchmarks in SPEC CPU 2006, acquire over 300 DFGs hotspots by using LLVM compiler tool, and divide them into 15 clusters based on graph similarity. Secondly, we introduce a function instruction set architecture （FISC） and illustrate how DFG accelerators can be integrated with a processor core and how they can be used by applications. Our results show that the proposed DFG clustering and FISC design can speed up SPEC benchmarks 6.2X on average.

关键词： dataflow graph many-accelerator clustering function instruction set architecture

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：